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This paper shows that compressed sensing realized by means of regularizeddeconvolution and the Finite Isotropic Wavelet Transform is effective andreliable in hard X-ray solar imaging.  The method utilizes the Finite Isotropic Wavelet Transform with Meyerfunction as the mother wavelet. Further, compressed sensing is realized byoptimizing a sparsity-promoting regularized objective function by means of theFast Iterative Shrinkage-Thresholding Algorithm. Eventually, the regularizationparameter is selected by means of the Miller criterion.  The method is applied against both synthetic data mimicking theSpectrometer/Telescope Imaging X-rays (STIX) measurements and experimentalobservations provided by the Reuven Ramaty High Energy Solar SpectroscopicImager (RHESSI). The performances of the method are compared with the resultsprovided by standard visibility-based reconstruction methods.  The results show that the application of the sparsity constraint and the useof a continuous, isotropic framework for the wavelet transform provide anotable spatial accuracy and significantly reduce the ringing effects due tothe instrument point spread functions.

Solar hard X-ray imaging by means of Compressed Sensing and Finite Isotropic Wavelet Transform